Malicious attacks detection in crowded areas using deep learning-based approach

Fouzi Harrou, Mohamad Hittawe, Ying Sun, Ouadi Beya

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

With the increasing need to ensure people's safety in crowded areas, the development of a systematic anomaly detection mechanism is becoming indispensable. Here are a few examples of recent malicious attacks targeting crowded areas in big cities: in 2016, a truck driver attacked and killed 84 persons walking in the promenade in Nice, France; and on 19 December, 2016, a truck was deliberately driven into the Christmas market, in Berlin, Germany, killing 12 people and injuring 56 others. These attacks demonstrate the need for efficient monitoring systems to avoid such devastating attacks. To do so, early detection and prevention abilities are vital. Detecting and localizing abnormal events in crowded scenes is important and has significant implications in video surveillance applications. Video surveillance can be challenging, as abnormal events can be unpredictable and changing, based on the context. Accurately detecting and localizing anomalies in videos is a powerful tool that can help to improve security and understand the behavior of anomalies. In this paper, we present an automated visionbased monitoring scheme specifically designed for atypical event-detection and localization in crowded areas.
Original languageEnglish (US)
Pages (from-to)57-62
Number of pages6
JournalIEEE Instrumentation and Measurement Magazine
Volume23
Issue number5
StatePublished - Aug 1 2020

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